SIDE MANAGEMENT ♦ Antonio Capozza 1 , Biagio D’Apice 2 , Daniele Gallo 2 , Carmine Landi 2 , Giuseppe Mauri 1 , Nicola Rignano 2 1 CESI SpA, Via Rubattino 54, 20134 Milano tel. +39-0221251fax +39-0221255520 Email:giuseppe.mauri@cesi.it 2 Dip. Ing, dell’Informazione, Seconda Università di Napoli, via Roma 29 – 81031 Aversa (CE) Tel.: +390815010349 – fax: +390815037042 Email:carmine.landi@unina2.it The paper examines the possibility to create a structure for electrical energy demand management and control of distributed loads. It starts with a synthetic description of Demand Side Management (DSM), then it proposes an innovative cluster based system architecture viable to achieve DSM objectives [1],[2]. In recent years many solutions have been investigated for the management and control of distributed loads, however most prominent ones for reaching objectives of DSM, appear to be hierarchical and clustered system architectures. This paper will broadly describe the second approach, highlighting main advantages and showing how such a solution is compatible with the consolidated architecture of widely diffused Automated Meter Reading (AMR) systems[3]. Fig. 1 Load Diagrams 12345678910112131415161718192021222324 Time(h) P o w e r (M W ) Standard Load Diagram Re-Shaped Load Diagr am The DSM of the electric power encompasses all actions of planning, actuating and monitoring of deliberated initiatives, aiming to stimulate final users to modify modalities and entity of electric power demand, hopefully without decreasing the level of services presently enjoyed by final users. DSM final goal is to re-shape load diagram as showed in figure 1 [4],[5]. Several actors like final users (User/Client), electrical utilities and the society may benefit from DSM implementation and technology for distributed load’s control. However for making available all the information necessary for acting on loads and implementing DSM strategies without decreasing services, final user’s premises need to be reached with high speed and reliable communication channels. Hence the communication infrastructure has to be chosen according to criteria like reliability, availability, cost and compatibility with already existing devices. In the last ♦ This work has been partially supported by MAP (Italian Ministry for Productive Activities) in the frame of Public Interest Energy Research Project named "Ricerca di Sistema" (MAP Decree of 28 February 2003). Fig. 2 A widely diffused AMR infrastructure PUBBLICATO A5018613 (PAD – 637047)

decade, the aim of increasing profits, led many distribution utilities to look at new services to be delivered through their distribution networks, this brought to use the existing distribution grids also as a communication infrastructure. One of the first services making use of the distribution grid as communication infrastructure is Automated Meter Reading (AMR) and customer remote management. An outline of a widely diffused AMR system infrastructure is shown in Figure 2. Such an infrastructure can also be the base for a clustered system for electrical energy demand management. The proposed solution for demand side management and distributed load control is a clustered architecture. A cluster is a group of devices that collaborate to execute a common application, appearing as a single system both to users and to higher level applications. The advantage of such a structure is that it allows execution of multiple operations in parallel, hence it alleviates the workload of each node. A multilevel clustered system avoids that faults and failures in a node (or in a cluster) may cause the failure of the whole system, additionally it allows the distribution of services, actions and specific functions over a given technological infrastructure and applications for system control and management such those deployed in the automation of the present electrical systems. Deploying a multilevel clustered system allows: • the complete control and the continuous monitoring of the whole system up to the higher level; • the widest managerial autonomy within lower levels; A multilevel cluster system actually works as cellular system in which, each unit has wide decisional autonomy in resources’ management, while it will still get the vision of the whole system. A cluster system optimises the acquisition’s process, distribution and monitoring of resources. Advantages of a cluster system are: • possibility of complete management and control of distributed computational resources; • availability of a technological infrastructure that allows a careful analyse of information flows necessary for problem solving and manage losses of resources; • possibility to provide fast reactions in emergency cases by triggering alerts for specific recovery applications; • Possibility to offer to final users a distributed computational power that can be used to provide new and future services. A typical cluster system architecture is showed in figure 3: Each box represents a cluster with both decision and actuation functions. The three highest levels make the most important decisional infrastructure with software both for load control and energy metering, the three lowest levels host measuring units, sensors and devices that send feedback to high level nodes. Basically starting from level 6 and going up, we go from the actual measuring and actuation devices to information’s management. This architecture allows to: • introduce new functions by hardware and software enhancement, hence adding new nodes and upgrading software version; • increase the whole infrastructure computation capability by adding nodes on the same level (horizontal expansion of the cluster for each level). A clustered architecture like the one previously described can be adapted to the structure of an electrical system [6]-[11]. The clustered system for an electrical grid is composed by 4 Levels. Each level has following functions: • level 1–(National Control Centre) In emergency circumstances may request a load reduction/augmentation to level 2. The reduction/augmentation may take place either implementing a proper DSM strategy, hence leaving energised critical loads and/or sending “increase/decrease” requests to distributed generators; • level 2–(Distribution Control Centres) It takes requests from level 1 and sends them to level 3. It defines tariffs and DSM strategies. These are based upon information collected from: I) the market (day ahead and adjustment market); II) propensity of final users to consume energy as function of tariffs; III) additional variable (holidays, weekdays, season and weather conditions). Actual tariffs are sent to level 3. In addition, level 2 may independently decide (e.g. according to exercise or maintenance necessity) to send load reduction requests to groups of MV/LV substations (level 3) keeping energised critical loads according to the current DSM strategy; • level 3–(MV/LV Substations) Every evening level 3 receives (from level 2): electrical energy tariffs and DSM strategies for the day after. It actuates the strategy defined by level 2, making also a real time control of devices on level 4, monitoring energy consumption, power quality parameters and the MV/LV transformer. It may send to final users requests for lightening load, coming from upper levels; • level 4–(Devices at the customer premise) According to the contract subscribed by each final user with the distribution utility, the device at the customer premise may receive electricity tariffs from level 3, signals regarding the availability of the distribution utility to buy back electrical energy, real time power reductions orders, control messages to manage loads and microgenerators. Concentrator Concentrator Concentrator Concentrator Supervision System Directors Nodes System for Multiuser Multiuser User User User User User User User User User User User User Level 6 Level 5 Level 4 Level 3 Level 2 Level 1 Fig. 3 Cluster System

Communication between levels is bi-directional, based both on wireless (GSM, GPRS, UMTS) and/or wired (PLC, Optical Fibre) channels [12]-[18]. The clustered structure and logical architecture described is showed in fig. 3 and 4 References [1] Jerry W. Evans: Energy Management System Survey of Architectures. 1989 IEEE. [2] A. Capozza: Methodologies for DSM in the electric industry: technical and economic evaluations (Italian language). CESI report: SFR-A0/021337. SFR-ELTEC-SCEL-2000-03-001, Research for the Italian Electrical System (First period), 2000. [3] G. Mauri: Analysis of technologies for digital meters and associated communications. CESI Report: FIA-A3/042272 (Project Rete21/SITAR/DSM/M3.1), Research for the Italian Electrical System (second period), Dec. 2003. [4] G. Mauri: Applicability of the current and future technologies to Demand Modulation. CESI Report: FIA-A4501811 (Project: Rete21/SITAR/DSM/M3.2), Research for the Italian Electrical System (second period), Jun. 2004. [5] C. Chemelli, W. Grattieri: Analysis of resources for the Demand modulation. CESI report: A4506017 (Project: EXTRA), Research for the Italian Electrical System (second period), Jun 2004. [6] G.Bucci, E.Fiorucci, C.Landi: Digital Measurement Station for Power Quality Analysis in Distributed Environments. IEEE Trans. on I&M, vol. 52, n .1, Febrary 2003. [7] G. Bucci, E.Fiorucci, C.Landi, G.Ocera: Architecture of digital wireless data communication network for distributed sensor applications, Measurement, vol. 32, Gen. 2003. [8] Ramon Canal, Joan-Manuel Parcerisa, Antonio González: A Cost-Effective Clustered Architecture. Departament d’Arquitectura de Computadors Universitat Politècnica de Catalunya. [9] Frank Dehne, Todd Eavis, Andrew Rau-Chaplin: A Cluster Architecture for Parallel Data Warehousing. Carleton University Dalhousie University Dalhousie University Ottawa, Canada Halifax, Canada Halifax, Canada, 2001 IEEE. [10] José González and Antonio González: Dynamic Cluster Resizing. Intel Barcelona Research Center, 2003 IEEE. [11] Richard Rabbat, Tom McNeal, Tim Burke: A High-Availability Clustering Architecture with Data Integrity Guarantees. Mission Critical Linux, 2001 IEEE. [12] A. Capozza: Effects of load control on the development of LV and MV grids (Italian language). CESI report: A4510027 (Project: ECORET/CONCA), Research for the Italian Electrical System (second period), Dec. 2004. [13] L. Croci & R. Viadana: Home automation applications for load management (Italian language). CESI report: A4503064 (Project: ECORET/CONCA/CIB), Research for the Italian Electrical System (second period), Dec. 2004. [14] L. Capetta: Broadband PLC to set up a Telecommunication Infrastructure – International Conference on Present and Future Trends of transmission systems and telecommunication 4-6Dec.2002, New Delhi (India), 2002. [15] L.Capetta, G.Mauri, F.Cesena, R.Napolitano: State of the Art and Perspectives of the PLC technology. AEI, Vol. 90, October-November 2003. [16] G. Bucci, C.Landi: A robust algorithm for measurement on MSK signals. IEEE Instr. and Meas. Technology Conference, IMTC 2000, Baltimora, 1-4 May, 2000. [17] D. Castaldo, D. Gallo, C. Landi: Collaborative Multisensor Network Architecture Based On Smart Web Sensor for Power Quality Applications. 2004 IEEE IMTC, Como (Italy), 18-20 May, 2004. [18] T. Hou, T. Tsai: An Access-Based Clustering Protocol for Multihop Wireless Ad Hoc Networks. IEEE Journal on selected areas in communications, VOL. 19, NO. 7, July 2001. Fig. 4 DSM Functions distributed at the various nodes of the Electrical system